class_docstr: Wrapping network def for a specified return_to_condition.
code: def set_return_to_condition(self, return_multiplier=1.0):
      return_to_condition = (
          self._max_return - self._min_return
      ) * return_multiplier + self._max_return
      self._return_to_condition = jnp.array(
          return_to_condition, dtype=jnp.float32
      )
  
imports: import functools
  from absl import logging
  from dopamine.jax import losses
  from dopamine.jax.agents.dqn import dqn_agent
  from dopamine.jax.agents.rainbow import rainbow_agent
  from dopamine.labs.offline_rl.jax import offline_rainbow_agent
  import gin
  import jax
  import jax.numpy
  import numpy
  import optax
  import tensorflow
label_desc: Use image node_id 2 for calling the WrappedNetworkDef obj's underlying member method code with example usage: obj.set_return_to_condition(return_multiplier) without return types